Task Allocation of Multiple Robotic Fishes Based on Self-Organizing Map Neural Network

Abstract:

For a water polo ball game there are multiple water polos and multiple robotic fishes in each team, seeking a reasonable task allocation plan is the key point to win the game. To resolve the problem, this paper proposed a multi-target task allocation method based on the Self-organizing map (SOM) neural network. This method takes the position of the water polos as the input vector, competes and compares the position of the water polos and robotic fishes, outputs the corresponding robotic fish of each water polo. The robotic fish will move toward the target water polo when the weight was adjusted, and will finally reach the target water polo. Simulations show that the score of the team using this method is higher than another team. The results prove the correctness and reliability of this method.

• Periodical: Applied Mechanics and Materials (Volume 563)
• Pages: 308-311
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.563.308
• Online since: May 2014
• Authors: Yu Lian Jiang*
• Keywords: Multiple Robotic Fishes, Neural Network (NN), Self-Organizing Map, Task Allocation

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